Quality indicators of clinical cancer care (QC<sub>3</sub>) in colorectal cancer
Bibliographic record
Abstract
OBJECTIVES: Assessing the quality of cancer care (QoCC) has become increasingly important to providers, regulators and purchasers of care worldwide. The aim of this study was to develop evidence-based quality indicators (QIs) for colorectal cancer (CRC) to be applied in a population-based setting. DESIGN: A comprehensive evidence-based literature search was performed to identify the initial list of QIs, which were then selected and developed using a two-step-modified Delphi process involving two multidisciplinary expert panels with expertise in CRC care, quality of care and epidemiology. SETTING: The QIs of the clinical cancer care (QC3) population-based project, which involves all the public and private hospitals and clinics present on the territory of Canton Ticino (South Switzerland). PARTICIPANTS: Ticino Cancer Registry, The Colorectal Cancer Working Group (CRC-WG) and the external academic Advisory Board (AB). MAIN OUTCOME MEASURES: Set of QIs which encompass the whole diagnostic-treatment process of CRC. RESULTS: Of the 149 QIs that emerged from 181 sources of literature, 104 were selected during the in-person meeting of CRC-WG. During the Delphi process, CRC-WG shortened the list to 89 QI. AB finally validated 27 QIs according to the phase of care: diagnosis (N=6), pathology (N=3), treatment (N=16) and outcome (N=2). CONCLUSIONS: Using the validated Delphi methodology, including a literature review of the evidence and integration of expert opinions from local clinicians and international experts, we were able to develop a list of QIs to assess QoCC for CRC. This will hopefully guarantee feasibility of data retrieval, as well as acceptance and translation of QIs into the daily clinical practice to improve QoCC. Moreover, evidence-based selected QIs allow one to assess immediate changes and improvements in the diagnostic-therapeutic process that could be translated into a short-term benefit for patients with a possible gain both in overall and disease-free survival.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.096 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".